Report preparation program and report preparation method

ABSTRACT

A computer-readable recording medium storing therein a report preparation program that causes at least one of storages and a computer coupled to the at least one of storages to execute a process for calculating an individual-feature quantity with respect to an input output per second (IOPS) of the at least one of storages for each piece of time-series data included in a time-series data group with respect to the IOPS; statistically processing the calculated individual-feature quantity of the time-series data group; calculating an entire-feature quantity based on the statistically processing; referring to a learning model generated based on at least one of learning time-series data groups and contents of a report for the at least one of learning time-series data groups, the learning model representing a relationship between an entire-feature quantity and contents of the report; and outputting information on contents of the report corresponding the calculated entire-feature quantity.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2018-43580, filed on Mar. 9, 2018,the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein are related a report preparationprogram and a report preparation method.

BACKGROUND

Generally, it may be desired to prepare a report for a time-series datagroup. For example, it is desirable to prepare a report in whichperformance logs indicating time changes such as a read input output persecond (IOPS) and a write IOPS in infrastructure equipment is collectedand what feature the performance logs represents is described.

As the related art, a method includes, for example, specifying a targetkeyword representing target data, selecting a target template to be usedfor expressing the target data based on the category of the targetkeyword, and generating a target text representing the target data. Forexample, provided is a technique of inputting motion data correspondingto a new test into a performance model peculiar to the engine type,electronically analyzing the output of the performance model, andelectronically generating at least one summary report on an enginehealth status based on the analyzed output.

Examples of the related art include Japanese Laid-open PatentPublication No. 2016-91078 and Japanese Laid-open Patent Publication No.2017-146299.

SUMMARY

According to an aspect of the embodiments, a computer-readable recordingmedium storing therein a report preparation program that causes at leastone of storages and a computer coupled to the at least one of storagesto execute a process, the process includes calculating anindividual-feature quantity with respect to an input output per second(IOPS) of the at least one of storages for each piece of time-seriesdata included in a time-series data group with respect to the IOPS;statistically processing the calculated individual-feature quantity tocalculate an entire-feature quantity of the accepted time-series datagroup; and referring to a learning model generated based on at least oneof time-series data groups and contents of a report for the at least oneof time-series data groups, the learning model representing arelationship between an entire-feature quantity and contents of a reportand outputting information on contents of a report corresponding acalculated entire-feature quantity.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an explanatory diagram illustrating an example of a reportpreparation method according to an embodiment;

FIG. 2 is an explanatory diagram illustrating an example of a reportpreparation system;

FIG. 3 is a block diagram illustrating a hardware configuration exampleof an information processing device;

FIG. 4 is an explanatory diagram illustrating an example of storagecontents of a time-series data table;

FIG. 5 is an explanatory diagram illustrating an example of storagecontents of an individual-feature quantity table;

FIG. 6 is an explanatory diagram illustrating an example of storagecontents of an entire-feature quantity table;

FIG. 7 is an explanatory diagram illustrating an example of storagecontents of an appearance flag table;

FIG. 8 is an explanatory diagram illustrating an example of storagecontents of an attention level table;

FIG. 9 is an explanatory diagram illustrating an example of storagecontents of a comment classification table;

FIG. 10 is an explanatory diagram illustrating an example of storagecontents of a learning model table;

FIG. 11 is an explanatory diagram illustrating the relationship betweenvarious data;

FIG. 12 is a block diagram illustrating a hardware configuration exampleof a client device;

FIG. 13 is a block diagram illustrating a functional configurationexample of the information processing device;

FIG. 14 is a block diagram illustrating a specific functionalconfiguration example of the information processing device;

FIG. 15 is an explanatory diagram illustrating a flow of operations ofthe information processing device;

FIG. 16 is an explanatory diagram (part 1) illustrating an example ofclassifying comments;

FIG. 17 is an explanatory diagram (part 2) illustrating an example ofclassifying comments;

FIG. 18 is an explanatory diagram illustrating an example of calculatingindividual-feature quantity;

FIG. 19 is an explanatory diagram illustrating an example of a method ofcalculating individual-feature quantity;

FIG. 20 is an explanatory diagram (Part 1) illustrating a specificexample of a calculation method;

FIG. 21 is an explanatory diagram (Part 2) illustrating a specificexample of the calculation method;

FIG. 22 is an explanatory diagram (part 3) illustrating a specificexample of the calculation method;

FIG. 23 is an explanatory diagram illustrating an example of calculatingan entire-feature quantity of a learning time-series data group;

FIG. 24 is an explanatory diagram representing features of a learningtime-series data group;

FIG. 25 is an explanatory diagram (part 1) illustrating an example ofgenerating a learning model;

FIG. 26 is an explanatory diagram (part 2) illustrating an example ofgenerating the learning model;

FIG. 27 is an explanatory diagram illustrating the relationship betweena learning model and a learning time-series data group;

FIG. 28 is an explanatory diagram illustrating an example of calculatingan entire-feature quantity of a target time-series data group;

FIG. 29 is an explanatory diagram (part 1) illustrating an example ofcalculating an attention level of a target time-series data group;

FIG. 30 is an explanatory diagram (part 2) illustrating an example ofcalculating an attention level of the target time-series data group;

FIG. 31 is an explanatory diagram illustrating an example of selecting atype of comment described in a report;

FIG. 32 is an explanatory diagram illustrating an example of generatinga comment described in a report;

FIG. 33 is an explanatory diagram illustrating a specific example of anoutput result;

FIG. 34 is a flowchart illustrating an example of a learning modelgeneration processing procedure; and

FIG. 35 is a flowchart illustrating an example of a report preparationprocessing procedure.

DESCRIPTION OF EMBODIMENTS

In the background art, it is difficult to prepare a report representingthe entire feature of the time-series data group. For example, a reportin which time-series data having a relatively high importance level andtime-series data having a relatively low importance level in thetime-series data group are equally handled and a comment is prepared andadded, thereby resulting in the report in which it is difficult tounderstand which part represents the entire feature of the time-seriesdata group. Hereinafter, the embodiment of a report preparation programand a report preparation method will be described in detail withreference to the drawings.

An Embodiment of Report Preparation Method

FIG. 1 is an explanatory diagram illustrating an example of a reportpreparation method according to the embodiment. An informationprocessing device 100 is a computer that accepts a time-series datagroup. The time-series data group includes, for example, plural piecesof time-series data having an identical attribute. The attribute is, forexample, a time zone in which each piece of data included in thetime-series data is acquired or measured.

A service of preparing a report on the time-series data group may bemade. For example, a service may be made in which a report is preparedon a time-series data group including time-series data representing timechange such as a read IOPS and a write IOPS in infrastructure equipment,thereby making it possible to grasp operational problems with theinfrastructure equipment.

On the other hand, techniques may be offered in which a comment for eachpiece of time-series data included in the time-series data group isprepared and a report describing the comment for each piece oftime-series data is prepared. For example, a technique may be offered inwhich a feature quantity is calculated for each piece of time-seriesdata and a comment corresponding to the feature quantity calculated foreach piece of time-series data is prepared.

However, when preparing a report on the time-series data group, it maybe preferable to prepare a report representing the entire feature of thetime-series data group. For example, it may be preferable to prepare areport describing comments in consideration of a relationship betweentime-series data, an importance level of time-series data, and so forth.For example, it may be preferable to describe a comment in which one ofthe time-series data has a larger data variation than the othertime-series data. For example, it may be preferable to describe acomment on time-series data with momentary data variation withoutdescribing a comment on time-series data with relatively few features.

In this case, in the above technique, a report representing a featurefor each piece of time-series data included in the time-series datagroup is prepared, and the report does not take into consideration therelationship between the time-series data and the importance level ofthe time-series data. For example, a report in which time-series datadetermined to have a relatively high importance level and time-seriesdata determined to have a relatively low importance level are equallyhandled and a comment is prepared and added, thereby resulting in thereport in which it is difficult to understand which part represents theentire feature of the time-series data group. As a result, it isdifficult to grasp which time-series data has a relatively highimportance level and which time-series data is preferable to check. Itis difficult to grasp the relationship between the time-series data.

Therefore, in the present embodiment, a report preparation method willbe described in which it is possible to support the preparation of thereport by statistically processing the individual-feature quantity foreach piece of time-series data to calculate the entire-feature quantityof the time-series data group and outputting information on the reportcorresponding to the entire-feature quantity.

In FIG. 1, the information processing device 100 stores a learning model110 representing the relationship between the entire-feature quantityand the contents of the report. The entire-feature quantity is a featurequantity calculated for the time-series data group. The learning model110 is generated based on, for example, at least one of time-series datagroups 120 and the contents of a report 121 for the at least one oftime-series data groups 120. The learning model 110 includes a treestructure model or a mathematical expression model. The time-series datagroup 120 is for learning.

The information processing device 100 calculates the individual-featurequantity for each piece of time-series data included in a time-seriesdata group 130. The time-series data group 130 is a target for which thepreparation of the report is supported. The information processingdevice 100 statistically processes the calculated individual-featurequantity and calculates the entire-feature quantity of the acceptedtime-series data group 130. The entire-feature quantity is, for example,a statistical value of the individual-feature quantity. The statisticalvalues include, for example, a maximum value, a minimum value, anaverage value, a mode value, and a median value.

The information processing device 100 refers to the learning model 110and outputs information 140 on the contents of the report correspondingto the calculated entire-feature quantity. The information 140 on thecontents of the report is, for example, a preparation index of a commentdescribed in the report. The information on the contents of the reportmay be, for example, the contents of the report itself.

From this, the information processing device 100 may make it easier toprepare a report representing the entire feature of the time-series datagroup 130. Therefore, the information processing device 100 may easilygrasp, for example, which part represents the entire feature of thetime-series data group 130. For example, the information processingdevice 100 may easily grasp which time-series data has a relatively highimportance level, and which time-series data is preferable to check,thereby easily grasping the relationship between the time-series data.The information processing device 100 may reduce the work burden ofpreparing the report representing the entire feature of the time-seriesdata group 130.

An Example of Report Preparation System 200

Next, with reference to FIG. 2, an example of a report preparationsystem 200 to which the information processing device 100 illustrated inFIG. 1 is applied will be described.

FIG. 2 is an explanatory diagram illustrating an example of the reportpreparation system 200. In FIG. 2, the report preparation system 200includes an information processing device 100 and a client device 201.

In the report preparation system 200, the information processing device100 and the client device 201 are connected via a wired or a wirelessnetwork 210. The network 210 is, for example, a local area network(LAN), a wide area network (WAN), the Internet, or the like.

The information processing device 100 stores a learning modelrepresenting the relationship between the entire-feature quantity andthe contents of the report. For example, the information processingdevice 100 accepts a plurality of combinations of learning time-seriesdata groups and reports, generates and stores a learning model based onthe combinations of the accepted learning time-series data groups andthe accepted reports. The information processing device 100 accepts, forexample, a combination of learning time-series data groups and reportsbased on an operation input by a user.

The information processing device 100 accepts the target time-seriesdata group. For example, the information processing device 100 receivesthe target time-series data group from the client device 201. Theinformation processing device 100 calculates the individual-featurequantity for each piece of time-series data included in the targettime-series data group, statistically processes the calculatedindividual-feature quantity, and calculates the entire-feature quantityof the target time-series data group.

The information processing device 100 refers to the learning model andoutputs information on the contents of the report corresponding to thecalculated entire-feature quantity. For example, the informationprocessing device 100 transmits information on the contents of thereport to the client device 201. The information processing device 100is, for example, a server, a personal computer (PC), or the like.

The client device 201 is a computer that acquires a time-series datagroup. The client device 201 acquires the time-series data group, forexample, based on an operation input by a user. The client device 201transmits the acquired time-series data group to the informationprocessing device 100. The client device 201 receives and outputsinformation on the report corresponding to the time-series data groupfrom the information processing device 100. The client device 201 is,for example, a server, a PC, a tablet terminal, a smartphone, or thelike.

The case where the information processing device 100 is different fromthe client device 201 has been described, but the present embodiment isnot limited to this case. For example, the information processing device100 may be integrated with the client device 201. In this case, forexample, the information processing device 100 accepts the targettime-series data group based on an operation input by a user.

The case where the information processing device 100 generates alearning model and outputs information on the contents of the reportwith reference to the learning model has been described, but the presentembodiment is not limited to this case. For example, the informationprocessing device 100 that generates a learning model and theinformation processing device 100 that outputs information on thecontents of the report with reference to the learning model may beindependent, and may cooperate with each other.

Hardware Configuration Example of Information Processing Device 100

Next, a hardware configuration example of the information processingdevice 100 will be described with reference to FIG. 3.

FIG. 3 is a block diagram illustrating a hardware configuration exampleof the information processing device 100. In FIG. 3, the informationprocessing device 100 includes a central processing unit (CPU) 301, amemory 302, a network interface (I/F) 303, a recording medium I/F 304,and a recording medium 305. Each component is connected by a bus 300.

The CPU 301 controls the entire information processing device 100. Thememory 302 includes, for example, a read only memory (ROM), a randomaccess memory (RAM), a flash ROM and so forth. For example, the flashROM or the ROM stores various programs, and the RAM is used as a workarea of the CPU 301. The program stored in the memory 302 is loaded intothe CPU 301 to cause the CPU 301 to execute the coded processing. Thememory 302 stores, for example, various tables described later withreference to FIGS. 4 to 10.

The network I/F 303 is connected to the network 210 via a communicationline, and is connected to another computer via the network 210. Thenetwork I/F 303 controls the interface between the network 210 and theinside components, and controls input and output of data from anothercomputer. The network I/F 303 may include, for example, a modem, and aLAN adapter.

The recording medium I/F 304 controls reading/writing of data from/tothe recording medium 305 under the control of the CPU 301. The recordingmedium I/F 304 is, for example, a disk drive, a solid state drive (SSD),or a universal serial bus (USB) port. The recording medium 305 is anonvolatile memory that stores data written under the control of therecording medium I/F 304. The recording medium 305 is, for example, adisk, a semiconductor memory, a USB memory, or the like. The recordingmedium 305 may be detachable from the information processing device 100.For example, the recording medium 305 may store various tables describedlater in FIGS. 4 to 10.

In addition to the above-described components, the informationprocessing device 100 may include a keyboard, a mouse, a display, aprinter, a scanner, a microphone, a speaker, and so forth. Theinformation processing device 100 may include a plurality of recordingmedium I/Fs 304 and a plurality of recording media 305. The informationprocessing device 100 may not include the recording medium I/F 304 orthe recording medium 305.

Storage Contents of Time-Series Data Table 400

Next, an example of storage contents of a time-series data table 400will be described with reference to FIG. 4. The time-series data table400 is implemented by, for example, a storage area such as the memory302 or the recording medium 305 of the information processing device 100illustrated in FIG. 3.

FIG. 4 is an explanatory diagram illustrating an example of storagecontents of the time-series data table 400. As illustrated in FIG. 4,the time-series series data table 400 has fields of a data group ID, adata ID, a time o'clock, and contents. The time-series data table 400stores time-series data by setting information for each piece of data inrespective fields.

The data group ID for identifying the time-series data group is set inthe field of the data group ID. The data ID for identifying thetime-series data included in the time-series data group is set in thedata ID field. The time at which the data included in the time-seriesdata is acquired is set in the time o′clock field. The contents of thedata included in the time-series data is set in the contents field. Inthe example of FIG. 4, the contents are I0 for at least one of storages.

Storage Contents of individual-feature quantity Table 500

Next, an example of storage contents of an individual-feature quantitytable 500 will be described with reference to FIG. 5. Theindividual-feature quantity table 500 is implemented by, for example, astorage area such as the memory 302 or the recording medium 305 of theinformation processing device 100 illustrated in FIG. 3.

FIG. 5 is an explanatory diagram illustrating an example of storagecontents of the individual-feature quantity table 500. As illustrated inFIG. 5, the individual-feature quantity table 500 has fields of a datagroup ID, a data ID, and an individual-feature quantity. In theindividual-feature quantity table 500, information on theindividual-feature quantity is stored as a record by setting informationfor each piece of time-series data in respective fields.

The data group ID for identifying the time-series data group is set inthe field of the data group ID. The data ID for identifying thetime-series data included in the time-series data group is set in thedata ID field. The individual-feature quantity calculated fromtime-series data are set in the field of the individual-featurequantity. In the example of FIG. 5, the individual-feature quantitiesare a var and a spike. The var is a feature quantity indicating themagnitude of data variation, and is a variation level. The spike is afeature quantity indicating the magnitude of instantaneous datavariation, and means a spike level. Storage Contents of Entire-FeatureQuantity Table 600

Next, an example of storage contents of an entire-feature quantity table600 will be described with reference to FIG. 6. The entire-featurequantity table 600 is implemented by, for example, a storage area suchas the memory 302 or the recording medium 305 of the informationprocessing device 100 illustrated in FIG. 3.

FIG. 6 is an explanatory diagram illustrating an example of storagecontents of the entire-feature quantity table 600. As illustrated inFIG. 6, the entire-feature quantity table 600 has fields of a data groupID and an entire-feature quantity. In the entire-feature quantity table600, information on the entire-feature quantity is stored as a record bysetting information for each time-series data group in respectivefields.

The data group ID for identifying the time-series data group is set inthe field of the data group ID. The entire-feature quantity of thetime-series data group calculated from the individual-feature quantityfor each piece of time-series data is set in the field of theentire-feature quantity. In the example of FIG. 6, the entire-featurequantities are a var maximum value, a var minimum value, a spike maximumvalue, a spike minimum value, and so forth.

Storage Contents of Appearance Flag Table 700

Next, an example of storage contents of an appearance flag table 700will be described with reference to FIG. 7. The appearance flag table700 is implemented by, for example, a storage area such as the memory302 and the recording medium 305 of the information processing device100 illustrated in FIG. 3.

FIG. 7 is an explanatory diagram illustrating an example of storagecontents of the appearance flag table 700. As illustrated in FIG. 7, theappearance flag table 700 has fields of a data group ID and anappearance flag for each type of comment. In the appearance flag table700, appearance flag information is stored as a record by settinginformation for each type of comment in respective fields.

The data group ID for identifying the time-series data group is set inthe field of the data group ID. Flag information indicating whether apredetermined type of comment appears in a report corresponding to thetime-series data group is set in the field of appearance flags for eachtype of comment. The flag information indicates that the predeterminedtype of comment does not appear if the flag information represents 0 andthat the predetermined type of comment appears if the flag informationrepresents 1. In the example of FIG. 7, the types of comments are anoverall maximum value/average value, a momentary high load, aconsecutive high load, a small variation, a small load and so forth.

Storage Contents of Attention Level Table 800

Next, an example of storage contents of an attention level table 800will be described with reference to FIG. 8. The attention level table800 is implemented by, for example, a storage area such as the memory302 or the recording medium 305 of the information processing device 100illustrated in FIG. 3.

FIG. 8 is an explanatory diagram illustrating an example of storagecontents of the attention level table 800. As illustrated in FIG. 8, theattention level table 800 has fields of a data group ID and an attentionlevel for each type of comment. In the attention level table 800,attention level information is stored as a record by setting informationfor each type of comment in respective fields.

The data group ID for identifying the time-series data group is set inthe field of the data group ID. The attention level which is an indexvalue indicating whether it is preferable to include a predeterminedtype of comment in a report is set as a preparation index of the reportcorresponding to the time-series data group in the field of theattention level for each type of comment. In the example of FIG. 8, thetypes of comments are overall maximum value/average value, a momentaryhigh load, a consecutive high load, the small variation, a small loadand so forth.

Storage Contents of Comment Classification Table 900

Next, an example of storage contents of a comment classification table900 will be described with reference to FIG. 9. The commentclassification table 900 is implemented by, for example, a storage areasuch as the memory 302 or the recording medium 305 of the informationprocessing device 100 illustrated in FIG. 3.

FIG. 9 is an explanatory diagram illustrating an example of storagecontents of the comment classification table 900. As illustrated in FIG.9, the comment classification table 900 has fields of a classificationand a comment. In the comment classification table 900, information onthe comment classification is stored as a record by setting informationfor each type of comment in respective fields.

The type classifying the comment is set in the field of theclassification. Templates of a predetermined type of comments are set inthe comment field. For example, the template of the comment for theoverall maximum value/average value indicates that “the maximum value isa <value 1>, the average value is a <value 2>”. Appropriate values maybe substituted into the value 1 and the value 2.

Storage Contents of Learning Model Table 1000

Next, an example of storage contents of a learning model table 1000 willbe described with reference to FIG. 10. The learning model table 1000 isimplemented by, for example, a storage area such as the memory 302 orthe recording medium 305 of the information processing device 100illustrated in FIG. 3.

FIG. 10 is an explanatory diagram illustrating an example of storagecontents of the learning model table 1000. As illustrated in FIG. 10,the learning model table 1000 has fields of a node ID, a parent node ID,parent node True/False, a feature quantity, a threshold, an attentionlevel when True, and an attention level when False. In the learningmodel table 1000, information on the learning model is stored as arecord by setting of information for each node in respective fields.

The node ID for identifying a node included in the learning model is setin the field of the node ID. The node ID for identifying a parent nodefor a predetermined node is set in the field of the parent node ID. Flaginformation indicating whether the predetermined node is a child node onTrue side or a child node on False side with respect to the parent nodeis set in the field of the parent node True/False.

The type of the feature quantity used for the determination conditionwhere the type is indicated by the predetermined node is set in thefeature quantity field. The threshold used for a determination conditionwhere the threshold is indicated by a predetermined node is set in thethreshold field. The attention level indicated by a leaf node when Truebranch is the leaf node when True where the feature quantity is equal toor larger than the threshold is set in the field of attention level whenTrue. The attention level indicated by a leaf node when False branch isthe leaf node when False where the feature quantity is smaller than thethreshold is set in the field of attention level when False.

Relationship of Various Data

FIG. 11 is an explanatory diagram illustrating the relationship betweenvarious data.

As illustrated in FIG. 11, the time-series data group 1101 correspondsto the time-series data 1102 in a 1:n manner. The time-series data 1102corresponds to the time o′clock data content 1103 in a 1:n manner. Thetime-series data 1102 corresponds to the individual-feature quantity1104 in a 1:1 manner. The individual-feature quantity 1104 correspondsto the entire-feature quantity 1105 in an n:1 manner, and thetime-series data group 1101 corresponds to the entire-feature quantity1105 in a 1:1 manner. The entire-feature quantity 1105 corresponds tothe appearance flag information 1106 in a 1:1 manner, and theentire-feature quantity 1105 corresponds to the attention levelinformation 1107 in a 1:1 manner.

Hardware Configuration Example of Client Device 201

Next, with reference to FIG. 12, a hardware configuration example of theclient device 201 included in the report preparation system 200illustrated in FIG. 2 will be described.

FIG. 12 is a block diagram illustrating a hardware configuration exampleof the client device 201. In FIG. 12, the client device 201 includes aCPU 1201, a memory 1202, a network I/F 1203, a recording medium I/F1204, a recording medium 1205, a display 1206, and an input device 1207.The respective components are connected to each other by a bus 1200.

The CPU 1201 controls the entire client device 201. The memory 1202includes, for example, a ROM, a RAM, a flash ROM, and so forth. Forexample, the flash ROM or the ROM stores various programs, and the RAMis used as a work area of the CPU 1201. The program stored in the memory1202 is loaded into the CPU 1201 to cause the CPU 1201 to execute thecoded processing.

The network I/F 1203 is connected to the network 210 via a communicationline, and is connected to another computer via the network 210. Thenetwork I/F 1203 controls the interface between the network 210 and theinside components, and controls input and output of data from anothercomputer. The network I/F 1203 may include, for example, a modem, and aLAN adapter.

The recording medium I/F 1204 controls reading/writing of data from/tothe recording medium 1205 under the control of the CPU 1201. Therecording medium I/F 1204 is, for example, a disk drive, an SSD, a USBport, or the like. The recording medium 1205 is a nonvolatile memorythat stores data written under the control of the recording medium I/F1204. The recording medium 1205 is, for example, a disk, a semiconductormemory, a USB memory, or the like. The recording medium 1205 may bedetachable from the client device 201.

The display 1206 displays data such as a document, an image, andfunction information, in addition to a cursor, an icon and a tool box.The display 1206 may be implemented by, for example, a cathode ray tube(CRT), a liquid crystal display, an organic electroluminescence (EL)display, or the like. The input device 1207 has keys for inputtingletters, numerals, various instructions, and so forth, and data is inputthrough the input device 1207. The input device 1207 may be a keyboard,a mouse, or the like, or may be a touch panel input pad, a ten-key pad,or the like.

In addition to the above-described components, the client device 201 mayinclude, for example, a printer, a scanner, a microphone, a speaker, orthe like. The client device 201 may include a plurality of recordingmedium I/Fs 1204 and a plurality of recording media 1205. The clientdevice 201 may not include the recording medium I/F 1204 or therecording medium 1205.

Functional Configuration Example of Information Processing Device 100

Next, a functional configuration example of the information processingdevice 100 will be described with reference to FIG. 13.

FIG. 13 is a block diagram illustrating a functional configurationexample of the information processing device 100. The informationprocessing device 100 includes a storage unit 1300, an acquisition unit1301, a calculation unit 1302, a generation unit 1303, and an outputunit 1304.

The storage unit 1300 is implemented by, for example, a storage areasuch as the memory 302 and the recording medium 305 illustrated in FIG.3. Hereinafter, a case where the storage unit 1300 is included in theinformation processing device 100 will be described, but the presentembodiment is not limited to this case. For example, the storage unit1300 may be included in a device different from the informationprocessing device 100, and storage contents of the storage unit 1300 maybe referred to from the information processing device 100.

The acquisition unit 1301 to the output unit 1304 function as an exampleof a control unit. The functions of the acquisition unit 1301 to theoutput unit 1304 are implemented by causing the CPU 301 to execute aprogram stored in a storage area such as the memory 302 and therecording medium 305 illustrated in FIG. 3, or by using the network I/F303. The results processed by each functional unit are stored in, forexample, a storage area such as the memory 302 or the recording medium305 illustrated in FIG. 3.

The storage unit 1300 stores various kinds of information referred to orupdated in the processing by each functional unit. The storage unit 1300stores, for example, a learning model. The learning model is a modelrepresenting the relationship between the entire-feature quantity andthe contents of the report. For example, the learning model represents,for each type of comment, the relationship between the entire-featurequantity and the index value indicating the appearance frequency of thetype of comment in the report. The learning model includes, for example,a tree structure model or a mathematical expression model. The reportincludes, for example, a comment on at least one of pieces oftime-series data.

The storage unit 1300 may store a combination of a learning time-seriesdata group and a report. The storage unit 1300 may store a targettime-series data group. The time-series data group is plural pieces oftime-series data having an identical attribute. The attribute is, forexample, a time zone in which each piece of data included in thetime-series data is acquired or measured. The storage unit 1300 maystore a machine learning method. The machine learning method is a methodof generating a learning model.

The storage unit 1300 may store the individual-feature quantity for eachpiece of time-series data included in the time-series data group. Theindividual-feature quantity is a feature quantity representing themagnitude of the data variation of the time-series data. The storageunit 1300 may store the entire-feature quantity of the time-series datagroup. The entire-feature quantity is a statistical value of theindividual-feature quantity. The statistical value is, for example, aminimum value, a maximum value, an average value, a mode value, a medianvalue, or the like. For example, the storage unit 1300 may store varioustables described later with reference to FIGS. 4 to 10. Thus, thestorage unit 1300 may make various kinds of information available toeach functional unit.

The acquisition unit 1301 acquires various kinds of information used forthe processing of each functional unit and outputs them to eachfunctional unit. For example, the acquisition unit 1301 may acquirevarious kinds of information used for the processing of each functionalunit from the storage unit 1300. For example, the acquisition unit 1301may acquire various kinds of information used for the processing of eachfunctional unit from a device different from the information processingdevice 100. For example, the acquisition unit 1301 may acquire variouskinds of information used for the processing of each functional unitfrom a device different from the information processing device 100 andstore the information in the storage unit 1300.

For example, the acquisition unit 1301 may accept a learning time-seriesdata group, or may accept a target time-series data group based on anoperation input by a user and output the two time-series data groups tothe calculation unit 1302. For example, the acquisition unit 1301 mayreceive the learning time-series data group, or may receive the targettime-series data group from the client device 201 and output the twotime-series data groups to the calculation unit 1302. As a result, theacquisition unit 1301 may make various kinds of information available toeach functional unit.

Operation in Generating Learning Model

First, the operations of the calculation unit 1302 to the generationunit 1303 when generating a learning model in response to acquiring thelearning time-series data group by the acquisition unit 1301 will bedescribed. The calculation unit 1302 to the generation unit 1303 may notperform the operations when generating the learning model if thelearning model has been stored in the storage unit 1300.

The calculation unit 1302 calculates the individual-feature quantity foreach piece of time-series data included in the time-series data group.The individual-feature quantities are, for example, the var and thespike. For example, the calculation unit 1302 calculates at least one oftypes of individual-feature quantities for each piece of time-seriesdata included in the time-series data group with respect to eachtime-series data group of at least one of learning time-series datagroups. A specific example of calculating the individual-featurequantity will be described later with reference to FIGS. 18 to 22, forexample.

The calculation unit 1302 statistically processes the calculatedindividual-feature quantity to calculate the entire-feature quantity ofthe time-series data group. The entire-feature quantities are, forexample, a var maximum value, a var minimum value, a spike maximumvalue, and a spike minimum value. For example, the calculation unit 1302statistically processes each type of individual-feature quantities withrespect to each time-series data group of at least one of learningtime-series data groups to calculate at least one of types ofentire-feature quantities. Types of individual-feature quantities maynot correspond to types of entire-feature quantities in a 1:1 manner. Aspecific example of calculating the entire-feature quantity will bedescribed later, for example, with reference to FIG. 23. Thus, thecalculation unit 1302 may provide the information used for generatingthe learning model to the generation unit 1303.

The generation unit 1303 generates a learning model. The generation unit1303 generates the learning model based on, for example, at least one oflearning time-series data groups and the contents of the report for theat least one of time-series data groups. For example, the generationunit 1303 calculates, for each type of comment, the entire-featurequantity and the index value indicating the appearance frequency of thetype of comment in the report using the machine learning method, andgenerates the learning model. A specific example of generating thelearning model will be described later, for example, with reference toFIG. 25 and FIG. 26. As a result, the generation unit 1303 may make thelearning model available.

Operation in Supporting Preparation of Report

Next, the operations of the calculation unit 1302 to the generation unit1303 when supporting the preparation of a report in response toacquiring the target time-series data group by the acquisition unit 1301will be described.

The calculation unit 1302 calculates the individual-feature quantity foreach piece of time-series data included in the time-series data group.The individual-feature quantities are, for example, the var and thespike. For example, the calculation unit 1302 calculates at least one oftypes of individual-feature quantities for each piece of time-seriesdata included in the target time-series data group. A specific exampleof calculating the individual-feature quantity will be described later,for example, with reference to FIGS. 18 to 22 and FIG. 28.

The calculation unit 1302 statistically processes the calculatedindividual-feature quantity to calculate the entire-feature quantity ofthe time-series data group. The entire-feature quantities are, forexample, a var maximum value, a var minimum value, a spike maximumvalue, and a spike minimum value. For example, the calculation unit 1302statistically processes each type of individual-feature quantities withrespect to the target time-series data group, and calculates at leastone of types of entire-feature quantities. Types of individual-featurequantities may not correspond to types of entire-feature quantities in a1:1 manner. A specific example of calculating the entire-featurequantity will be described later, for example, with reference to FIG. 23and FIG. 28. As a result, the calculation unit 1302 may provideinformation to be input to the learning model to the generation unit1303.

The generation unit 1303 refers to the learning model and generatesinformation on the contents of the report corresponding to thecalculated entire-feature quantity. For example, the generation unit1303 refers to the learning model and generates an index valuecorresponding to the calculated entire-feature quantity for each type ofcomment. A specific example of generating the index value will bedescribed later, for example, with reference to FIG. 29 and FIG. 30. Asa result, the generation unit 1303 may support the preparation of thereport.

For example, the generation unit 1303 may refer to the learning modeland may generate a comment on at least at least one of pieces oftime-series data included in the accepted time-series data group basedon the index value corresponding to the calculated entire-featurequantity for each type of comment. A specific example of generating acomment will be described later, for example, with reference to FIG. 31and FIG. 32. As a result, the generation unit 1303 may support thepreparation of the report.

The generation unit 1303 may select at least one of pieces oftime-series data for generating the comment among the acceptedtime-series data group, for example, based on the calculatedindividual-feature quantity or a feature quantity for each piece oftime-series data included in the time-series data group where thefeature quantity is other than the calculated individual-featurequantity. For example, the generation unit 1303 may select, as thetime-series data for generating the comment, the time-series data whichis the calculation source of the individual-feature quantity used forthe type of comment for which the index value is the maximum.

For example, the generation unit 1303 may refer to the maximum value ineach piece of time-series data that is not used as an index value, andmay select the time-series data having the largest maximum value astime-series data for generating the comment. As a result, the generationunit 1303 may generate the comment on the time-series data having arelatively high importance level, making it easy to prepare the reportdescribing the comment in consideration of importance level oftime-series data. The generation unit 1303 may reduce the processingamount.

The output unit 1304 outputs various kinds of information. The outputformat is, for example, display on a display, print out to a printer,transmission to an external device by the network I/F 303, or storage ina storage area such as the memory 302 or the recording medium 305.

For example, the output unit 1304 may output the learning modelgenerated by the generation unit 1303. The output unit 1304 stores, forexample, the learning model generated by the generation unit 1303 in thestorage unit 1300. As a result, the output unit 1304 may support thepreparation of the report. As a result, the output unit 1304 may makethe learning model available.

For example, the output unit 1304 may output information on the contentsof the report generated by the generation unit 1303. The output unit1304 causes to display, for example, information on the contents of thereport generated by the generation unit 1303 on the display of theclient device 201. As a result, the output unit 1304 may support thepreparation of the report.

For example, the output unit 1304 may output the results processed byeach functional unit. Thus, the output unit 1304 may notify the user ofthe results processed by each functional unit, and may support themanagement and operation of the information processing device 100, forexample, update of the setting values of the information processingdevice 100, whereby it is possible to improve the usability of theinformation processing device 100.

Specific Functional Configuration Example of Information ProcessingDevice 100

Next, a specific functional configuration example of the informationprocessing device 100 will be described with reference to FIG. 14.

FIG. 14 is a block diagram illustrating a specific functionalconfiguration example of the information processing device 100. Theinformation processing device 100 includes an individual-featurequantity extraction processing unit 1401, an entire-feature quantityextraction processing unit 1402, a learning model generation processingunit 1403, and an attention level output processing unit 1404.

The individual-feature quantity extraction processing unit 1401 acceptsthe input of at least one of learning time-series data groups 1410. Theat least one of learning time-series data groups 1410 are stored in thetime-series data table 400, for example. When the at least one oflearning time-series data groups 1410 are input, the individual-featurequantity extraction processing unit 1401 calculates individual-featurequantity for each piece of time-series data included in the learningtime-series data groups 1410, and outputs an individual-feature quantitygroup 1420 for each time-series data group 1410. For example, theindividual-feature quantity group 1420 for each learning time-seriesdata group 1410 is stored in the individual-feature quantity table 500.

The entire-feature quantity extraction processing unit 1402 accepts theinput of the individual-feature quantity group 1420 for each learningtime-series data group 1410. When the individual-feature quantity group1420 for each learning time-series data group 1410 is input, theentire-feature quantity extraction processing unit 1402 calculates andoutputs the entire-feature quantity 1430 for each learning time-seriesdata group 1410. The entire-feature quantity 1430 for each learningtime-series data group 1410 is stored, for example, using theentire-feature quantity table 600.

The learning model generation processing unit 1403 accepts the input ofthe entire-feature quantity 1430 for each learning time-series datagroup 1410 and the input of an appearance flag 1440 for each type ofcomment. The appearance flag 1440 for each type of comment is stored,for example, using the appearance flag table 700. The learning modelgeneration processing unit 1403 inputs, into the machine learningmethod, the entire-feature quantity 1430 for each learning time-seriesdata group 1410 as an explanatory variable and the appearance flag 1440for each type of comment as a target variable to create a learning model1450. The learning model 1450 is stored, for example, using the learningmodel table 1000.

The attention level output processing unit 1404 accepts the input of atarget time-series data group 1460. The target time-series data group1460 is stored in, for example, the time-series data table 400. When thetarget time-series data group 1460 is input, the attention level outputprocessing unit 1404 calls the individual-feature quantity extractionprocessing unit 1401 and causes the attention level output processingunit 1404 to calculate the individual-feature quantity group for eachpiece of time-series data included in the target time-series data group1460. The individual-feature quantity group for each piece oftime-series data included in the target time-series data group 1460 isstored in, for example, the individual-feature quantity table 500.

The attention level output processing unit 1404 accepts the input of theindividual-feature quantity for each piece of time-series data includedin the target time-series data group 1460. The attention level outputprocessing unit 1404 calls the entire-feature quantity extractionprocessing unit 1402 and causes the entire-feature quantity extractionprocessing unit 1402 to calculates the entire-feature quantity of thetarget time-series data group 1460 based on the individual-featurequantity for each piece of time-series data included in the targettime-series data group 1460. The entire-feature quantity of the targettime-series data group 1460 is stored, for example, using theentire-feature quantity table 600.

The attention level output processing unit 1404 accepts the input of theentire-feature quantity of the target time-series data group 1460. Theattention level output processing unit 1404 inputs the entire-featurequantity of the target time-series data group 1460 in the learning model1450, and calculates and outputs an attention level 1470 for each typeof comment. The attention level 1470 is an index value indicatingwhether it is preferable to include a predetermined type of comment in areport as a preparation index of the report corresponding to the targettime-series data group 1460. The attention level 1470 is stored using,for example, the attention level table 800.

The attention level output processing unit 1404 may further prepare thereport based on the attention level 1470 for each type of comment. As aresult, the information processing device 100 may make it easier toprepare a report representing the entire feature of the targettime-series data group 1460. The information processing device 100 mayreduce the work burden of preparing the report representing the entirefeature of the target time-series data group 1460.

Flow of Operation of Information Processing Device 100

Next, the flow of the operation of the information processing device 100will be described with reference to FIG. 15.

FIG. 15 is an explanatory diagram illustrating the flow of the operationof the information processing device 100. In FIG. 15, the informationprocessing device 100 operates in a learning phase for generating alearning model and an attention level calculation phase for supportingthe preparation of the report. First, the operation in the learningphase will be described.

Learning Phase

Using a document analysis technique, the information processing device100 classifies the comments 1510 described in the report correspondingto each of at least one of learning time-series data groups 1500, andspecifies the types of comments 1510. The types of comments 1510 are,for example, overall maximum value/average value, momentary high load,consecutive high load, small variation, small load, very small load, andso forth. The information processing device 100 may specify the types ofcomments 1510 based on an operation input by a user. The informationprocessing device 100 generates flag information indicating whether thecomment appears in a report corresponding to the learning time-seriesdata group 1500 for each type of comment 1510. (15-1)

The information processing device 100 calculates the individual-featurequantity for each piece of time-series data included in the time-seriesdata group 1500 with respect to each time-series data group 1500 of theat least one of learning time-series data groups 1500. Theindividual-feature quantities are a var and a spike. The var is afeature quantity indicating the magnitude of data variation, and is avariation level. The spike is a feature quantity indicating themagnitude of instantaneous data variation, and means a spike level.(15-2)

The information processing device 100 calculates the entire-featurequantity based on individual-feature quantity for each piece oftime-series data included in the time-series data group 1500 withrespect to each time-series data group 1500 of the at least one oflearning time-series data groups 1500. The entire-feature quantitiesare, for example, a var maximum value, a var minimum value, a spikemaximum value, and a spike minimum value. (15-3)

The information processing device 100 inputs the entire-feature quantityof the learning time-series data group 1500 as an explanatory variablein the machine learning method and inputs the appearance flag for eachtype of comment 1510 as target variables to generate a learning model1520. As a result, the information processing device 100 may make thelearning model 1520 available. (15-4)

Since the information processing device 100 generates the learning model1520 using the entire-feature quantity obtained by integrating theindividual-feature quantity of each piece of time-series data includedin the time-series data group, the relationship between the time-seriesdata in the time-series data group may be reflected in the learningmodel 1520. Since the information processing device 100 unifies thenumber of types of entire-feature quantities between the time-seriesdata groups 1500 and uses the entire-feature quantity as an explanatoryvariable, the information processing device 100 may make the learningmodel 1520 applicable regardless of the number of pieces of time-seriesdata included in the time-series data group 1500.

Attention Level Calculation Phase

The information processing device 100 calculates at least one of typesof individual-feature quantities for each piece of time-series dataincluded in a target time-series data group 1530. The informationprocessing device 100 calculates the entire-feature quantity of thetarget time-series data group 1530 based on at least one of types ofindividual-feature quantities calculated for each piece of time-seriesdata included in the target time-series data group 1530. (15-5)

The information processing device 100 inputs the entire-feature quantityof the target time-series data group 1530 in the learning model 1520,and calculates and outputs the attention level for each type of comment.For example, the information processing device 100 calculates theattention level “0.8” for the type of comment which is “momentary highload”. The information processing device 100 may prepare a report basedon the attention level for each type of comment. As a result, theinformation processing device 100 may make it easier to prepare a reportrepresenting the entire feature of the target time-series data group1530. (15-6)

Since the information processing device 100 inputs the entire-featurequantity obtained by integrating the individual-feature quantity of thetime-series data included in the time-series data group in the learningmodel 1520, the attention level reflecting the relationship between thetime-series data in the time-series data group may be calculated. Theinformation processing device 100 may reduce the work burden ofpreparing the report representing the entire feature of the targettime-series data group 1530. Even when label information or the likeuseful for preparing a comment is not assigned to each piece oftime-series data included in the target time-series data group 1530, theinformation processing device 100 may prepare a report representing theentire feature of the target time-series data group 1530.

Operation Example of Information Processing Device 100

Next, an operation example of the information processing device 100 willbe described with reference to FIGS. 16 to 33. For example, an exampleof operation in which the information processing device 100 generates alearning model will be described with reference to FIGS. 16 to 27, andan example of operation in which the information processing device 100supports the preparation of the report will be described with referenceto FIGS. 28 to 33. First, an example of classifying comments will bedescribed with reference to FIGS. 16 and 17.

FIGS. 16 and 17 are explanatory diagrams illustrating an example ofclassifying comments. In FIG. 16, the information processing device 100acquires reports 1601 to 1603. The reports 1601 to 1603 correspond tothe time-series data groups to which data group IDs 1 to 3 areallocated, respectively. The information processing device 100 extractscomments from the reports 1601 to 1603 and classifies the comments. Theinformation processing device 100 stores the result of classifyingcomments using the comment classification table 900. Next, thedescription of FIG. 17 will be made.

In FIG. 17, the information processing device 100 determines, for eachtype of comment, whether the type of comment appears in each of thereports 1601 to 1603. The information processing device 100 stores thedetermined result using the appearance flag table 700. An example ofcalculating the individual-feature quantity will be described withreference to FIG. 18.

FIG. 18 is an explanatory diagram illustrating an example of calculatingindividual-feature quantity. In FIG. 18, the information processingdevice 100 calculates individual-feature quantity normalized in therange of 0 to 1 for each piece of time-series data included in each ofthe time-series data groups to which the data group IDs 1 to ID 3 areallocated. The individual-feature quantities are, for example, the varand the spike. An example of a method of calculating theindividual-feature quantity will be described with reference to FIG. 19.

FIG. 19 is an explanatory diagram illustrating an example of a method ofcalculating individual-feature quantity. A table 1900 of FIG. 19illustrates the individual-feature quantity ID in association with themethod of calculating individual-feature quantity. As illustrated inFIG. 19, the var is a feature quantity indicating the magnitude of datavariation calculated from the maximum value−the minimum value. The spikeis a feature quantity indicating the magnitude of instantaneous datavariation calculated by (80% percentile value−median value)/(maximumvalue−median value).

The high_num is a feature quantity indicating a state of a high loadwhich is calculated from the maximum value of the frequency in which thedata value is larger than the average value consecutively. Thehigh_val_sum is calculated from the maximum value of Σ(datavalue−average value) in data in which the data value is larger than theaverage value consecutively. The ave_ratio is calculated from theaverage value in the time-series data/the average value in thetime-series data group. A specific example of the calculation methodwill be described with reference to FIGS. 20 to 22.

FIGS. 20 to 22 are explanatory diagrams illustrating specific examplesof the calculation method. A graph 2000 in FIG. 20 illustrates aspecific example of a method of calculating the high_num and thehigh_val_sum. The high_num is the number of data points whose values areconsecutively equal to or more than the average value. If there is aplurality of ranges where there is the number of data points whosevalues are consecutively equal to or more than the average value, thehigh_num is a maximum value of the number of data points whose valuesare consecutively equal to or more than the average value. Thehigh_val_sum is an area of a range where there is the number of datapoints whose values are consecutively equal to or more than the averagevalue. If there are a plurality of ranges where there is the number ofdata points whose values are consecutively equal to or more than theaverage value the high_val_sum is a maximum value of the areas of theranges where there is the number of data points whose values areconsecutively equal to or more than the average value. Next, thedescription of FIG. 21 will be made.

A graph 2100 of FIG. 21 illustrates a specific example of the method ofcalculating the ave_ratio. In the example of FIG. 21, the time-seriesdata group includes time-series data A, time-series data B, andtime-series data C. In this case, the ave_ratio for the time-series dataB is obtained by the formula of (the average value (b) of thetime-series data B)/(the average value (x) in the time-series datagroup). Next, the description of FIG. 22 will be made.

The graph 2200 in FIG. 22 illustrates a specific example of thecalculation method of the var and the spike. The var is obtained by theformula of (the maximum value−the minimum value). The spike is obtainedby the formula of (80% percentile value−median value)/(maximumvalue−median value), and indicates that the larger the value, the largerthe protrusion of the maximum value is. An example of calculating theentire-feature quantity of the learning time-series data group will bedescribed with reference to FIG. 23.

FIG. 23 is an explanatory diagram illustrating an example of calculatingthe entire-feature quantity of the learning time-series data group. InFIG. 23, the information processing device 100 calculates theentire-feature quantity of the time-series data group of the data groupID 1 based on a record 501 of the individual-feature quantity table 500corresponding to the data group ID 1, and adds a record 601 to theentire-feature quantity table 600. Similarly, the information processingdevice 100 calculates the entire-feature quantity of the time-seriesdata group of the data group ID 2 based on a record 502 of theindividual-feature quantity table 500 corresponding to the data group ID2, and adds a record 602 to the entire-feature quantity table 600.

Similarly, the information processing device 100 calculates theentire-feature quantity of the time-series data group of the data groupID 3 based on a record 503 of the individual-feature quantity table 500corresponding to the data group ID 3, and adds a record 603 to theentire-feature quantity table 600. In this manner, the informationprocessing device 100 may calculate entire-feature quantities having thesame number of types for each time-series data group irrespective of thenumber of time-series data included in the time-series data group, andmay handles the time-series data group uniformly. Features of thelearning time-series data group will be described with reference to FIG.24.

FIG. 24 is an explanatory diagram illustrating the features of thelearning time-series data group. A table 2400 of FIG. 24 illustrates therelationship between the entire-feature quantity of the learningtime-series data group corresponding to each of the data group IDs 1 to3, which are specified in the above, and the comment appearing in therecord corresponding to the learning time-series data group.

For example, what relationship exists between presence or absence of thecomment whose type is “small variation” and the entire-feature quantityobtained by integrating the individual-feature quantity of each piece ofthe time-series data included in the time-series data group isspecified. For this reason, the information processing device 100 mayreflect the relationship between the time-series data in the time-seriesdata group and may generate a learning model that is applicableirrespective of the number of time-series data included in thetime-series data group 1500. Next, an example of generating a learningmodel will be described with reference to FIGS. 25 and 26.

FIGS. 25 and 26 are explanatory diagrams illustrating an example ofgenerating the learning model. In FIG. 25, the information processingdevice 100 generates a learning model for each type of comment using amachine learning method. The machine learning method is, for example, aclassification and regression tree (CART).

For example, the information processing device 100 generates a learningmodel 2510 in which the entire-feature quantity is an input and theattention level of the comment whose type is “momentary high load” is anoutput. For example, the information processing device 100 generates alearning model 2520 in which the entire-feature quantity is an input andthe attention level of the comment whose type is “small variation” is anoutput. Next, with reference to the description of FIG. 26, an examplein which the machine learning method determines the attention level willbe described with the learning model 2510 as an example.

A table 2600 in FIG. 26 is a table in which points corresponding tocombinations of entire-feature quantities corresponding to eachtime-series data group of at least one of learning time-series datagroups are arranged in a space with the entire-feature quantity as theaxis. The color of the point indicates whether the comment whose type is“small variation” appears in the report corresponding to the time-seriesdata group. Black indicates no comment appear. White indicates thecomment appears.

In the machine learning method, the classification and regression treeas the learning model 2510 is generated with the maximum depth of theclassification and regression tree as 2. In the machine learning method,for example, the space with the entire-feature quantity as the axis isdivided so that a collection of time-series data groups in which thecomment whose type is “small variation” appears and a collection oftime-series data groups in which the comments whose type is “smallvariation” does not appear are efficiently divided. In the example ofFIG. 26, in the machine learning method, a space with the var minimumvalue and the spike maximum value, which are the entire-featurequantity, as the axes is divided into three regions.

In the machine learning method, the proportion of white points out ofthe points included in the divided region is calculated as the attentionlevel of the comment whose type is “small variation”. In the machinelearning method, a classification and regression tree to be the learningmodel 2510 is generated based on the value of the entire-featurequantity which is the boundary of the region and the calculatedattention level, and is stored using the learning model table 1000.Next, with reference to FIG. 27, the relationship between the learningtime-series data group and the learning model 2510 will be explained.

FIG. 27 is an explanatory diagram illustrating the relationship betweenthe learning model 2510 and the learning time-series data group. Asillustrated in FIG. 27, in the learning model 2510, in consideration ofthe relationship between the time-series data, the importance level ofthe time-series data, and so forth, it is possible to specify whether itis preferable to describe a comment whose type is “small variation” inthe report corresponding to the time-series data group.

Since the time-series data group of the data group ID 1 includes thetime-series data having the momentary high load, the importance level ofthe time-series data is relatively high, and the comment whose type is“small variation” may not be described. According to the learning model2510, the time-series data group of the data group ID 1 may beclassified into a leaf node 2702 based on the entire-feature quantity ofthe time-series data group of the data group ID 1. As a result, thelearning model 2510 makes it possible to calculate the attention level0.2 of the comment whose type is “small variation” with respect to thetime-series data group of the data group ID 1, and it is also possibleto specify that the comment whose type is “small variation” may not bedescribed.

Since in the time-series data group of the data group ID 2, alltime-series data has a small variation, it is preferable to describe thecomment whose type is “small variation”. According to the learning model2510, the time-series data group of the data group ID 2 may beclassified into a leaf node 2701 based on the entire-feature quantity ofthe time-series data group of the data group ID 2. As a result, thelearning model 2510 makes it possible to calculate the attention level0.8 of the comment whose type is “small variation” with respect to thetime-series data group of the data group ID 2, and it is possible tospecify that it is preferable to describe the comment whose type is“small variation”.

Since in the time-series data group of the data group ID 3, alltime-series data fluctuates greatly, it is preferable not to describethe comment whose type is “small variation”. According to the learningmodel 2510, the time-series data group of the data group ID 3 may beclassified into a leaf node 2703 based on the entire-feature quantity ofthe time-series data group of the data group ID 3. As a result, thelearning model 2510 makes it possible to calculate the attention level0.0 of the comment whose type is “small variation” with respect to thetime-series data group of the data group ID 3, and it is possible tospecify that it is preferable not to describe the comment whose type is“small variation”.

Next, with reference to FIGS. 28 to 33, description will be given of anoperation example in which the information processing device 100 acceptsthe target time-series data group and supports the preparation of thereport on the target time-series data group. First, an example ofcalculating the entire-feature quantity of the target time-series datagroup will be described with reference to FIG. 28.

FIG. 28 is an explanatory diagram illustrating an example of calculatingthe entire-feature quantity of the target time-series data group. Withrespect to the target time-series data group, as in FIGS. 18 to 23, theinformation processing device 100 calculates the individual-featurequantity to store the calculated individual-feature quantity using theindividual-feature quantity table 500, and calculates the entire-featurequantity to store the calculated entire-feature quantity using theentire-feature quantity table 600.

As a result, the information processing device 100 may calculate apredetermined number of types of entire-feature quantities irrespectiveof the number of pieces of time-series data included in the targettime-series data group, and may calculate the entire-feature quantitythat may be input to the learning model 2510. Next, an example ofcalculating the attention level of the target time-series data groupwill be described with reference to FIGS. 29 and 30.

FIGS. 29 and 30 are explanatory diagrams illustrating an example ofcalculating an attention level of the target time-series data group. InFIG. 29, the information processing device 100 inputs the entire-featurequantity of the target time-series data group to the learning model2510.

For example, the learning model 2510 classifies the target time-seriesdata group into the node with the node ID 1, which is the root node.Since the var minimum value of the entire-feature quantity of the targettime-series data group is less than 0.5 in the node of the node ID 1,the learning model 2510 determines that the result is True andclassifies the target time-series data group into the node of node ID 2.

Since the spike maximum value of the entire-feature quantity of thetarget time-series data group is not less than 0.3 in the node of nodeID 2, the learning model 2510 determines that the result is False andclassifies the target time-series data group into the node 2702, whichis a leaf node. The learning model 2510 outputs the attention level 0.2of the comment whose type is “small variation” indicated by the leafnode 2702. Next, the description of FIG. 30 will be made.

In FIG. 30, the information processing device 100 similarly calculatesthe attention level of the comment whose type is other than the typewhich is “small variation” by using a learning model corresponding tothe comment whose type is other than the type which is “smallvariation”. The information processing device 100 stores the calculatedattention level using the attention level table 800. Next, thedescription of FIG. 31 will be made.

FIG. 31 is an explanatory diagram illustrating an example of selectingthe type of comment described in the report. In FIG. 31, the informationprocessing device 100 selects the type of comment to be described in thereport based on the attention level. For example, the informationprocessing device 100 selects, as the type of comment to be described inthe report, the type whose attention level is the maximum, that is,“overall maximum value/average value”. The information processing device100 acquires a record 3100 corresponding to the selected type of commentfrom the comment classification table 900.

For example, the information processing device 100 may select, as thetype of comment to be described in the report, a type whose attentionlevel is equal to or greater than a certain level. For example, theinformation processing device 100 may select, as the type of comment tobe described in the report, a predetermined number of types in orderfrom the type whose attention level is the maximum. Next, thedescription of FIG. 32 will be made.

FIG. 32 is an explanatory diagram illustrating an example of generatingthe comment described in the report. The information processing device100 generates a selected type of comment. For example, the informationprocessing device 100 selects any time-series data included in thetime-series data group, and generates a comment using the template ofthe comments set in the acquired record 3100. In the example of FIG. 32,the information processing device 100 generate a comment 3200, that is,“the maximum value is 120, and the average value is 50”.

When selecting the time-series data for generating a comment, theinformation processing device 100 selects the time-series data based onthe individual-feature quantity of the time-series data, a featurequantity other than the individual-feature quantity, or the like. Forexample, when selecting the time-series data for generating a comment,the information processing device 100 selects the time-series data baseon the individual-feature quantity corresponding to the type of comment,the feature quantity corresponding to the type of comment not used forthe individual-feature quantity or the like.

Since, for example, the type is “overall maximum value/average value”,the information processing device 100 refers to the maximum value ofeach piece of time-series data in order to select the time-series datafor generating a comment. The information processing device 100 selectsthe time-series data having the largest maximum value among thetime-series data group. Upon generating the comment, the informationprocessing device 100 generates and outputs the report in which thecomment is described. Next, a specific example of the output result willbe described with reference to FIG. 33.

FIG. 33 is an explanatory diagram illustrating a specific example of theoutput result. In FIG. 33, the information processing device 100generates a comment with the time-series data group illustrated in atable 3300 as a target time-series data group, and outputs a report inwhich the comment is described. In the example of FIG. 33, theinformation processing device 100 outputs a report in which a comment3301 is described.

Although a technique may be conceivable in which a comment for eachpiece of time-series data is generated, a report in which a comment 3302is described is likely to be output with the technique. In this case, itis difficult for the user to grasp at a first glance which time-seriesdata has a relatively high importance level and which part represent afeature in any of the time-series data

On the other hand, since the information processing device 100 outputs areport in which the comment 3301 is described, it is possible to outputa report that makes it easy to grasp at a first glance the featuredevent and data among the time-series data group. Therefore, theinformation processing device 100 may easily grasp which part representsthe entire feature of the time-series data group. The informationprocessing device 100 may easily grasp which time-series data has arelatively high importance level, and which time-series data ispreferable to check, thereby easily grasping the relationship betweenthe time-series data.

In the examples of FIGS. 28 to 33, the case where the informationprocessing device 100 selects the type of comment to be described in thereport based on the attention level and prepares the report isdescribed, but the present embodiment is not limited to this case. Forexample, the information processing device 100 may change the displaymode for each type of comment described in the report based on theattention level. For example, the information processing device 100 mayhighlight a comment whose type has an attention level which is higherthan a certain level, and prepare a report that displays withoutdistinction a comment whose type has an attention level which is lessthan a certain level.

For example, the information processing device 100 may change thedisplay order for each type of comment described in the report based onthe attention level. For example, the information processing device 100may prepare a report in descending order from the comment whose type hasan attention level which is high.

Learning Model Generation Processing Procedure

Next, an example of the learning model generation processing procedureperformed by the information processing device 100 will be describedwith reference to FIG. 34. The learning model generation processing isimplemented, for example, by the CPU 301, a storage area such as thememory 302 and the recording medium 305, and the network I/F 303illustrated in FIG. 3.

FIG. 34 is a flowchart illustrating an example of the learning modelgeneration processing procedure. In FIG. 34, first, the informationprocessing device 100 classifies comments appearing in a plurality ofreports corresponding to a plurality of time-series data groups, andspecifies the type of the comments (step S3401).

Next, the information processing device 100 calculates the featurequantity of each piece of time-series data included in each time-seriesdata group of the plurality of time-series data groups and stores thecalculated feature quantity as the individual-feature quantity (stepS3402). The information processing device 100 calculates a summarystatistical value for each time-series data group among a plurality oftime-series data groups based on the individual-feature quantitycalculated for each time-series data included in the time-series datagroup, and stores the calculated summary statistical value as theentire-feature quantity (step S3403).

Next, in step S3404, the information processing device 100 inputs theentire-feature quantity as an explanatory variable in the machinelearning method, inputs the presence or absence of the appearance of thecomment for each type of comment as a target variable, and generates alearning model. The information processing device 100 ends the learningmodel generation processing. As a result, the information processingdevice 100 may make the learning model available.

Report Preparation Processing Procedure

Next, an example of the report preparation processing procedureperformed by the information processing device 100 will be describedwith reference to FIG. 35. The report preparation processing isimplemented, for example, by the CPU 301, a storage area such as thememory 302 and the recording medium 305, and the network I/F 303illustrated in FIG. 3.

FIG. 35 is a flowchart illustrating an example of the report preparationprocessing procedure. In FIG. 35, first, the information processingdevice 100 accepts the target time-series data group and calculates theentire-feature quantity of the target time-series data group (stepS3501).

Next, the information processing device 100 inputs the entire-featurequantity in the learning model, and acquires the attention level foreach type of comment as the output of the learning model (step S3502).The information processing device 100 prepares and outputs a reportbased on the attention level for each type of acquired comments (stepS3503). Thereafter, the information processing device 100 ends thereport preparation processing. Accordingly, the information processingdevice 100 may prepare the report and make it easy to grasp the entirefeature of the time-series data group.

As described above, the information processing device 100 may calculatethe individual-feature quantity for each piece of time-series dataincluded in the time-series data group. The information processingdevice 100 may statistically process the calculated individual-featurequantity and calculate the entire-feature quantity of the acceptedtime-series data group. The information processing device 100 may referto the learning model representing the relationship between theentire-feature quantity and the contents of the report, and outputinformation on the contents of the report corresponding to thecalculated entire-feature quantity. As a result, the informationprocessing device 100 may make it easier to prepare a reportrepresenting the entire feature of the time-series data group.

The information processing device 100 may use, for each type of comment,the learning model representing the relationship between theentire-feature quantity and the index value indicating the appearancefrequency of the type of comment in the report. The informationprocessing device 100 may output the index value corresponding to thecalculated entire-feature quantity for each type of comment. As aresult, the information processing device 100 makes it easy to graspwhich type of comment is preferable to generate in order to prepare thereport representing the entire feature of the time-series data group.

The information processing device 100 may generate and output thecomment on at least one of pieces of time-series data included in theaccepted time-series data group based on the index value correspondingto the calculated entire-feature quantity for each type of comment. As aresult, the information processing device 100 may prepare the reportrepresenting the entire feature of the time-series data group, and mayreduce the burden of preparing the report.

The information processing device 100 may select at least one of piecesof time-series data for generating the comment among the acceptedtime-series data group based on the calculated individual-featurequantity or a feature quantity for each piece of time-series dataincluded in the time-series data group where the feature quantity isother than the calculated individual-feature quantity. As a result, theinformation processing device 100 may select the time-series data fromwhich it is preferable to generate the comment, and makes it possiblefor the report to accurately represent the entire feature of thetime-series data group.

The information processing device 100 may use, as the individual-featurequantity, the feature quantity representing the magnitude of datavariation of the time-series data. The information processing device 100may use, as the entire-feature quantity, the minimum value and themaximum value of the individual-feature quantity. Thus, the informationprocessing device 100 may make it easier to prepare the reportdescribing the comment based on the magnitude of data variation.

The information processing device 100 may use plural pieces oftime-series data having the identical attribute as the time-series datagroup. As a result, the information processing device 100 may use apreferable time-series data group as a target for preparing the report.

The information processing device 100 may use, as the time-series datagroup, plural pieces of time-series data having common time zones inwhich each piece of data is acquired or measured. As a result, theinformation processing device 100 may use the time-series data group onthe common time zone, where the time-series data group is preferable asthe target for preparing the report.

The report preparation method described in this embodiment may beimplemented by executing a prepared program on a computer such as apersonal computer or a workstation. The report preparation programdescribed in the present embodiment is recorded on a computer readablerecording medium such as a hard disk, a flexible disk, a CD-ROM, an MO,a DVD, or the like, and is executed by being read from the recordingmedium by a computer. The report preparation program described in thepresent embodiment may be distributed via a network such as theInternet.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although at least one of embodiments ofthe present invention have been described in detail, it should beunderstood that the various changes, substitutions, and alterationscould be made hereto without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A computer-readable recording medium storingtherein a report preparation program that causes at least one ofstorages and a computer coupled to the at least one of storages toexecute a process, the process comprising: calculating anindividual-feature quantity with respect to an input output per second(IOPS) of the at least one of storages for each piece of time-seriesdata included in a time-series data group with respect to the IOPS;statistically processing the calculated individual-feature quantity ofthe time-series data group; calculating an entire-feature quantity basedon the statistically processing; referring to a learning model generatedbased on at least one of learning time-series data groups and contentsof a report for the at least one of learning time-series data groups,the learning model representing a relationship between an entire-featurequantity and contents of the report; and outputting information oncontents of the report corresponding the calculated entire-featurequantity.
 2. The computer-readable recording medium storing therein thereport preparation program according to claim 1, wherein the reportincludes a comment on at least one of pieces of time-series data,wherein the learning model represents, for each type of comment, arelationship between an entire-feature quantity and an index valueindicating an appearance frequency of the type of comment in a report,and wherein the outputting includes outputting an index valuecorresponding to a calculated entire-feature quantity for each type ofcomment.
 3. The computer-readable recording medium storing therein thereport preparation program according to claim 2, wherein the outputtingincludes generating and outputting a comment on at least one of piecesof time-series data included in the time-series data group based on anindex value corresponding to the calculated entire-feature quantity foreach type of comment.
 4. The computer-readable recording medium storingtherein the report preparation program according to claim 3, wherein theoutputting includes selecting at least one of pieces of time-series datafor generating a comment out of the time-series data group based on thecalculated individual-feature quantity or a feature quantity for eachpiece of time-series data included in the time-series series data group,the feature quantity being other than the calculated individual-featurequantity.
 5. The computer-readable recording medium storing therein thereport preparation program according to claim 1, wherein theindividual-feature quantity represents a magnitude of a data variationof the time-series data group, and wherein the entire-feature quantityrepresents a minimum value and a maximum value of the individual-featurequantity.
 6. The computer-readable recording medium storing therein thereport preparation program according to claim 1, wherein the time-seriesdata group is plural pieces of time-series data having an identicalattribute.
 7. The computer-readable recording medium storing therein thereport preparation program according to claim 6, wherein the attributeis a time zone in which each piece of data included in the time-seriesdata is acquired or measured.
 8. A method of preparing a report, themethod executed by a computer comprising: calculating anindividual-feature quantity with respect to an input output per second(IOPS) of a storage for each piece of time-series data included in atime-series data group with respect to the IOPS; statisticallyprocessing the calculated individual-feature quantity to calculate anentire-feature quantity of the time-series data group; referring to alearning model generated based on at least one of learning time-seriesdata groups and contents of a report for the at least one of learningtime-series data groups, the learning model representing a relationshipbetween an entire-feature quantity and contents of the report; andoutputting information on contents of the report corresponding thecalculated entire-feature quantity.
 9. A machine learning devicecomprising: a memory configured to store time series data and a learningmodel, the learning model representing a relationship between anentire-feature quantity of at least one learning time-series data groupand corresponding learning report; and a processor coupled to the memoryand configured to perform acquiring the target time-series data group;calculating an individual-feature quantity with respect to an inputoutput per second (IOPS) of infrastructure equipment for each piece oftime-series data included in the target time-series data group withrespect to the IOPS; statistically processing the calculatedindividual-feature quantity of the target time-series data group;calculating an entire-feature quantity based on the statisticallyprocessing; inputting the calculated entire-feature quantity to thelearning model to obtain a report for the target time-series data group,the report including at least one comment on target time-series datawithin the target time-series data group, the at least one commentprepared based on the calculated entire-feature quantity and attentionlevels assigned to categories of comments within the learning report;and outputting the obtained report.
 10. The machine learning deviceaccording to claim 9, wherein the corresponding learning report of eachof the at least one learning time series data group includes a pluralityof comments belonging to different comment categories and an attentionlevels are assigned for the comment categories within the learningreports, the attention levels being index values for determiningimportance of comments to be included in the obtained report dependingon the input calculated entire-feature quantity.
 11. The machinelearning device according to claim 10, wherein the learning modelrepresents, for each category of comment, a relationship between anentire-feature quantity and an index value indicating an appearancefrequency of the category of comment in the corresponding learningreport, and wherein the outputting includes outputting an index valuecorresponding to the calculated entire-feature quantity for eachcategory of comment.
 12. The machine learning device according to claim11, wherein the outputting includes generating and outputting a commenton at least one of pieces of time-series data included in thetime-series data group based on an index value corresponding to thecalculated entire-feature quantity for each type of comment.
 13. Themachine learning device according to claim 12, wherein the outputtingincludes selecting at least one piece of the target time-series data tobe associated with the at least one comment of the obtained report basedon the calculated individual-feature quantity or a feature quantity foreach piece of the target time-series data, the feature quantity beingother than the calculated individual-feature quantity.
 14. The machinelearning device according to claim 9, wherein the individual-featurequantity represents a magnitude of a data variation of the targettime-series data group, and wherein the entire-feature quantityrepresents a minimum value and a maximum value of the individual-featurequantity.
 15. The machine learning device according to claim 9, whereinthe at least one learning time-series data group and the targettime-series data group include a plurality of pieces of time-series datahaving an identical attribute.
 16. The machine learning device accordingto claim 15, wherein the attribute is a time zone in which each piece ofdata included in the time-series data is acquired or measured.